Sparse Token Routing in Efficient Transformers
Efficient-transformer research often motivates token pruning and adaptive computation with the claim that not all tokens require equal computational effort. We test this claim end to end using SEWN, a two-stream Transformer that routes tokens through either lightweight or full-capacity processing using a learned gate. Across our experiments, routing introduces negligible accuracy change relative to parameter-matched baselines, while the gate's token-importance signal depends critically on how it is learned. A static lexicon-seeded prior fails a counterfactual faithfulness test on BoolQ, whereas a fully contextual gate achieves highly significant separation ($p<10^{-10}$) on both evaluated...
| Authors | Sai Krishna Arthanari, JaeHyeong Chang, Chengzhe Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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